This article introduces an automatic approach for the segmentation of coloured natural scene images based on graphs and the propagation of labels originally designed for communities detection in complex networks. Images are initially pre‐segmented with super‐pixels, followed by feature extraction using colour information of each super‐pixels. The resulting graph consists of vertices which represent super‐pixels, whereas the edge weights are a measure of similarity between super‐pixels. The resulting segmentation corresponds to the propagation of labels among the vertices. In this article, three strategies for propagating labels have been formulated: (i) iterative propagation (ILP), (ii) recursive propagation (RLP) and (iii) a weighted recursive propagation (WRLP). The experiments have shown that the proposed methods, when compared to other state‐of‐the‐art methods, produce better results in terms of segmentation quality and processing time.

Autologous platelet concentrates are rich in growth factors and have shown potential for high anatomical success rates in macular hole treatment. However, no systematic review has yet assessed its impact. Therefore, this study aimed to conduct a comprehensive review and metaanalysis on the comparative efficacy and safety of autologous platelet concentrates in treating macular hole. A systematic review of the literature on autologous platelet concentrate therapy for macular hole was conducted in accordance with Preferred Reporting Items for Systematic

Feature Analysis has become a very critical task in data analysis and visualization. Graph structures are very flexible in terms of representation and may encode important information on features but are challenging in regards to layout being adequate for analysis tasks. In this study, we propose and develop similarity-based graph layouts with the purpose of locating relevant patterns in sets of features, thus supporting feature analysis and selection. We apply a tree layout in the first step of the strategy, to accomplish node placement and overview based on feature similarity. By drawing the remainder of the graph edges on demand, further grouping and relationships among features are revealed. We evaluate those groups and relationships in terms of their effectiveness in exploring feature sets for data analysis. Correlation of features with a target categorical attribute and feature ranking are added to support the task. Multidimensional projections are employed to plot the dataset based on selected attributes to reveal the effectiveness of the feature set. Our results have shown that the tree-graph layout framework allows for a number of observations that are very important in user-centric feature selection, and not easy to observe by any other available tool. They provide a way of finding relevant and irrelevant features, spurious sets of noisy features, groups of similar features, and opposite features, all of which are essential tasks in different scenarios of data analysis. Case studies in application areas centered on documents, images and sound data demonstrate the ability of the framework to quickly reach a satisfactory compact representation from a larger feature set.

A segmentação de imagens e, em visão computacional, uma tarefa de grande importância, para a qual existem várias abordagem. A complexidade de tais abordagens está relacionada à natureza da imagem e também...

Segmentação de imagens é uma tarefa importante em processamento de imagens usualmente empregada em tarefas mais complexas de visão computacional. Em abordagens de segmentação por agrupamento em grafos...

The importance of the classification of forest fires through computational methods lies in their applicability for early detection and timely warning to prevent their spread. According to the state of the art, the classification of forest fires was approached by proposals based on machine learning (ML) and deep learning (DL), more specifically on Convolutional Neural Networks (CNN). In practice, DL-based methods prove to be superior to traditional ML methods. Recently, FFireNet, a CNN model that presents optimal results for classifying images of forest fires, was proposed. On this basis, in thiswork, we present a methodology to create CNN models by transfer learning applied specifically to the classification of forest fires, where techniques such as data augmentation, transfer learning, transfer learning and fine tuning were included. The proposal allowed us to define E-FFireNet, an efficient model with superior results than FFireNet. The experiments showed classification results of 99,73 (accuracy), 99,48 (precision), 100,0 (recall) and 0,263 (error).